Quantization made by Richard Erkhov.

Monarch-7B - GGUF

Model creator: https://huggingface.co/mlabonne/
Original model: https://huggingface.co/mlabonne/Monarch-7B/

Name	Quant method	Size
Monarch-7B.Q2_K.gguf	Q2_K	2.53GB
Monarch-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
Monarch-7B.IQ3_S.gguf	IQ3_S	2.96GB
Monarch-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
Monarch-7B.IQ3_M.gguf	IQ3_M	3.06GB
Monarch-7B.Q3_K.gguf	Q3_K	3.28GB
Monarch-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
Monarch-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
Monarch-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
Monarch-7B.Q4_0.gguf	Q4_0	3.83GB
Monarch-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
Monarch-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
Monarch-7B.Q4_K.gguf	Q4_K	4.07GB
Monarch-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
Monarch-7B.Q4_1.gguf	Q4_1	4.24GB
Monarch-7B.Q5_0.gguf	Q5_0	4.65GB
Monarch-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
Monarch-7B.Q5_K.gguf	Q5_K	4.78GB
Monarch-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
Monarch-7B.Q5_1.gguf	Q5_1	5.07GB
Monarch-7B.Q6_K.gguf	Q6_K	5.53GB
Monarch-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: cc-by-nc-4.0 tags: - merge - mergekit - lazymergekit base_model: - mlabonne/OmniTruthyBeagle-7B-v0 - mlabonne/NeuBeagle-7B - mlabonne/NeuralOmniBeagle-7B model-index: - name: Monarch-7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.04 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Monarch-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.03 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Monarch-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.41 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Monarch-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 77.35 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Monarch-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 84.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Monarch-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 69.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Monarch-7B name: Open LLM Leaderboard

Monarch-7B

Update 13/02/24: Monarch-7B is the best-performing model on the YALL leaderboard.

Monarch-7B is a merge of the following models using LazyMergekit:

🏆 Evaluation

The evaluation was performed using LLM AutoEval on Nous suite. See the entire leaderboard here.

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
Monarch-7B 📄	62.68	45.48	77.07	78.04	50.14
teknium/OpenHermes-2.5-Mistral-7B 📄	52.42	42.75	72.99	52.99	40.94
mlabonne/NeuralHermes-2.5-Mistral-7B 📄	53.51	43.67	73.24	55.37	41.76
mlabonne/NeuralBeagle14-7B 📄	60.25	46.06	76.77	70.32	47.86
eren23/dpo-binarized-NeuralTrix-7B 📄	62.5	44.57	76.34	79.81	49.27
CultriX/NeuralTrix-7B-dpo 📄	62.5	44.61	76.33	79.8	49.24

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: mlabonne/OmniTruthyBeagle-7B-v0
    parameters:
      density: 0.65
      weight: 0.36
  - model: mlabonne/NeuBeagle-7B
    parameters:
      density: 0.6
      weight: 0.34
  - model: mlabonne/NeuralOmniBeagle-7B 
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Monarch-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	76.25
AI2 Reasoning Challenge (25-Shot)	73.04
HellaSwag (10-Shot)	89.03
MMLU (5-Shot)	64.41
TruthfulQA (0-shot)	77.35
Winogrande (5-shot)	84.61
GSM8k (5-shot)	69.07